[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6EKA2HWDG5FPYWTBAB0Y4KP":3,"skill-same:01M6EKA2HWDG5FPYWTBAB0Y4KP":112},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":33,"publishedAt":54,"linkOnly":55,"markdownContent":56,"markdownBody":57,"frontMatter":58,"refName":63,"commitSha":64,"skillDirectory":65,"forksCount":66,"repositoryHtmlUrl":67,"repositoryHomepageUrl":6,"ownerHtmlUrl":68,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":69,"install":70,"resources":83},"01M6EKA2HWDG5FPYWTBAB0Y4KP","prompt-engineer-toolkit",null,"Analyzes and rewrites prompts for better AI output, creates reusable prompt templates for marketing use cases (ad copy, email campaigns, social media), and structures end-to-end AI content workflows. Use when the user wants to improve prompts for AI-assisted marketing, build prompt templates, or optimize AI content workflows. Also use when the user mentions 'prompt engineering,' 'improve my prompts,' 'AI writing quality,' 'prompt templates,' or 'AI content workflow.'","ArogyaReddy\u002Falirezarezvani-claude-skills","ArogyaReddy","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F12032101?v=4","01M6EJA7DAM8950XYAD875F4NY","01M6EJA7JSMD9SN617EJHTF2BQ",39,"MIT","https:\u002F\u002Fgithub.com\u002FArogyaReddy\u002Falirezarezvani-claude-skills\u002Fblob\u002Fmain\u002Fmarketing-skill\u002Fprompt-engineer-toolkit\u002FSKILL.md",[17,22,25,30],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000019","营销推广","SCENARIO","使用场景",{"categoryNo":23,"categoryName":24,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":26,"categoryName":27,"taxonomyType":28,"taxonomyTypeDesc":29},"01JBSEEDCAT000000000000035","市场营销","PROFESSION","适用职业",{"categoryNo":31,"categoryName":32,"taxonomyType":28,"taxonomyTypeDesc":29},"01JBSEEDCAT000000000000036","内容创作者",[34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53],"toolkit","prompt engineer","engineer","prompt engineer toolkit","prompt","social media","end","campaigns","social","ai content workflow","optimize","use","writing","marketing","improve","Media","email","prompt templates","workflows","workflow","2026-11-10 10:12:30",false,"---\nname: \"prompt-engineer-toolkit\"\ndescription: \"Analyzes and rewrites prompts for better AI output, creates reusable prompt templates for marketing use cases (ad copy, email campaigns, social media), and structures end-to-end AI content workflows. Use when the user wants to improve prompts for AI-assisted marketing, build prompt templates, or optimize AI content workflows. Also use when the user mentions 'prompt engineering,' 'improve my prompts,' 'AI writing quality,' 'prompt templates,' or 'AI content workflow.'\"\nlicense: MIT\nmetadata:\n  version: 1.0.0\n  author: Alireza Rezvani\n  category: marketing\n  updated: 2026-03-06\n---\n\n# Prompt Engineer Toolkit\n\n## Overview\n\nUse this skill to move prompts from ad-hoc drafts to production assets with repeatable testing, versioning, and regression safety. It emphasizes measurable quality over intuition. Apply it when launching a new LLM feature that needs reliable outputs, when prompt quality degrades after model or instruction changes, when multiple team members edit prompts and need history\u002Fdiffs, when you need evidence-based prompt choice for production rollout, or when you want consistent prompt governance across environments.\n\n## Core Capabilities\n\n- A\u002FB prompt evaluation against structured test cases\n- Quantitative scoring for adherence, relevance, and safety checks\n- Prompt version tracking with immutable history and changelog\n- Prompt diffs to review behavior-impacting edits\n- Reusable prompt templates and selection guidance\n- Regression-friendly workflows for model\u002Fprompt updates\n\n## Key Workflows\n\n### 1. Run Prompt A\u002FB Test\n\nPrepare JSON test cases and run:\n\n```bash\npython3 scripts\u002Fprompt_tester.py \\\n  --prompt-a-file prompts\u002Fa.txt \\\n  --prompt-b-file prompts\u002Fb.txt \\\n  --cases-file testcases.json \\\n  --runner-cmd 'my-llm-cli --prompt {prompt} --input {input}' \\\n  --format text\n```\n\nInput can also come from stdin\u002F`--input` JSON payload.\n\n### 2. Choose Winner With Evidence\n\nThe tester scores outputs per case and aggregates:\n\n- expected content coverage\n- forbidden content violations\n- regex\u002Fformat compliance\n- output length sanity\n\nUse the higher-scoring prompt as candidate baseline, then run regression suite.\n\n### 3. Version Prompts\n\n```bash\n# Add version\npython3 scripts\u002Fprompt_versioner.py add \\\n  --name support_classifier \\\n  --prompt-file prompts\u002Fsupport_v3.txt \\\n  --author alice\n\n# Diff versions\npython3 scripts\u002Fprompt_versioner.py diff --name support_classifier --from-version 2 --to-version 3\n\n# Changelog\npython3 scripts\u002Fprompt_versioner.py changelog --name support_classifier\n```\n\n### 4. Regression Loop\n\n1. Store baseline version.\n2. Propose prompt edits.\n3. Re-run A\u002FB test.\n4. Promote only if score and safety constraints improve.\n\n## Script Interfaces\n\n- `python3 scripts\u002Fprompt_tester.py --help`\n  - Reads prompts\u002Fcases from stdin or `--input`\n  - Optional external runner command\n  - Emits text or JSON metrics\n- `python3 scripts\u002Fprompt_versioner.py --help`\n  - Manages prompt history (`add`, `list`, `diff`, `changelog`)\n  - Stores metadata and content snapshots locally\n\n## Pitfalls, Best Practices & Review Checklist\n\n**Avoid these mistakes:**\n1. Picking prompts from single-case outputs — use a realistic, edge-case-rich test suite.\n2. Changing prompt and model simultaneously — always isolate variables.\n3. Missing `must_not_contain` (forbidden-content) checks in evaluation criteria.\n4. Editing prompts without version metadata, author, or change rationale.\n5. Skipping semantic diffs before deploying a new prompt version.\n6. Optimizing one benchmark while harming edge cases — track the full suite.\n7. Model swap without rerunning the baseline A\u002FB suite.\n\n**Before promoting any prompt, confirm:**\n- [ ] Task intent is explicit and unambiguous.\n- [ ] Output schema\u002Fformat is explicit.\n- [ ] Safety and exclusion constraints are explicit.\n- [ ] No contradictory instructions.\n- [ ] No unnecessary verbosity tokens.\n- [ ] A\u002FB score improves and violation count stays at zero.\n\n## References\n\n- [references\u002Fprompt-templates.md](references\u002Fprompt-templates.md)\n- [references\u002Ftechnique-guide.md](references\u002Ftechnique-guide.md)\n- [references\u002Fevaluation-rubric.md](references\u002Fevaluation-rubric.md)\n- [README.md](README.md)\n\n## Evaluation Design\n\nEach test case should define:\n\n- `input`: realistic production-like input\n- `expected_contains`: required markers\u002Fcontent\n- `forbidden_contains`: disallowed phrases or unsafe content\n- `expected_regex`: required structural patterns\n\nThis enables deterministic grading across prompt variants.\n\n## Versioning Policy\n\n- Use semantic prompt identifiers per feature (`support_classifier`, `ad_copy_shortform`).\n- Record author + change note for every revision.\n- Never overwrite historical versions.\n- Diff before promoting a new prompt to production.\n\n## Rollout Strategy\n\n1. Create baseline prompt version.\n2. Propose candidate prompt.\n3. Run A\u002FB suite against same cases.\n4. Promote only if winner improves average and keeps violation count at zero.\n5. Track post-release feedback and feed new failure cases back into test suite.\n","# Prompt Engineer Toolkit\n\n## Overview\n\nUse this skill to move prompts from ad-hoc drafts to production assets with repeatable testing, versioning, and regression safety. It emphasizes measurable quality over intuition. Apply it when launching a new LLM feature that needs reliable outputs, when prompt quality degrades after model or instruction changes, when multiple team members edit prompts and need history\u002Fdiffs, when you need evidence-based prompt choice for production rollout, or when you want consistent prompt governance across environments.\n\n## Core Capabilities\n\n- A\u002FB prompt evaluation against structured test cases\n- Quantitative scoring for adherence, relevance, and safety checks\n- Prompt version tracking with immutable history and changelog\n- Prompt diffs to review behavior-impacting edits\n- Reusable prompt templates and selection guidance\n- Regression-friendly workflows for model\u002Fprompt updates\n\n## Key Workflows\n\n### 1. Run Prompt A\u002FB Test\n\nPrepare JSON test cases and run:\n\n```bash\npython3 scripts\u002Fprompt_tester.py \\\n  --prompt-a-file prompts\u002Fa.txt \\\n  --prompt-b-file prompts\u002Fb.txt \\\n  --cases-file testcases.json \\\n  --runner-cmd 'my-llm-cli --prompt {prompt} --input {input}' \\\n  --format text\n```\n\nInput can also come from stdin\u002F`--input` JSON payload.\n\n### 2. Choose Winner With Evidence\n\nThe tester scores outputs per case and aggregates:\n\n- expected content coverage\n- forbidden content violations\n- regex\u002Fformat compliance\n- output length sanity\n\nUse the higher-scoring prompt as candidate baseline, then run regression suite.\n\n### 3. Version Prompts\n\n```bash\n# Add version\npython3 scripts\u002Fprompt_versioner.py add \\\n  --name support_classifier \\\n  --prompt-file prompts\u002Fsupport_v3.txt \\\n  --author alice\n\n# Diff versions\npython3 scripts\u002Fprompt_versioner.py diff --name support_classifier --from-version 2 --to-version 3\n\n# Changelog\npython3 scripts\u002Fprompt_versioner.py changelog --name support_classifier\n```\n\n### 4. Regression Loop\n\n1. Store baseline version.\n2. Propose prompt edits.\n3. Re-run A\u002FB test.\n4. Promote only if score and safety constraints improve.\n\n## Script Interfaces\n\n- `python3 scripts\u002Fprompt_tester.py --help`\n  - Reads prompts\u002Fcases from stdin or `--input`\n  - Optional external runner command\n  - Emits text or JSON metrics\n- `python3 scripts\u002Fprompt_versioner.py --help`\n  - Manages prompt history (`add`, `list`, `diff`, `changelog`)\n  - Stores metadata and content snapshots locally\n\n## Pitfalls, Best Practices & Review Checklist\n\n**Avoid these mistakes:**\n1. Picking prompts from single-case outputs — use a realistic, edge-case-rich test suite.\n2. Changing prompt and model simultaneously — always isolate variables.\n3. Missing `must_not_contain` (forbidden-content) checks in evaluation criteria.\n4. Editing prompts without version metadata, author, or change rationale.\n5. Skipping semantic diffs before deploying a new prompt version.\n6. Optimizing one benchmark while harming edge cases — track the full suite.\n7. Model swap without rerunning the baseline A\u002FB suite.\n\n**Before promoting any prompt, confirm:**\n- [ ] Task intent is explicit and unambiguous.\n- [ ] Output schema\u002Fformat is explicit.\n- [ ] Safety and exclusion constraints are explicit.\n- [ ] No contradictory instructions.\n- [ ] No unnecessary verbosity tokens.\n- [ ] A\u002FB score improves and violation count stays at zero.\n\n## References\n\n- [references\u002Fprompt-templates.md](references\u002Fprompt-templates.md)\n- [references\u002Ftechnique-guide.md](references\u002Ftechnique-guide.md)\n- [references\u002Fevaluation-rubric.md](references\u002Fevaluation-rubric.md)\n- [README.md](README.md)\n\n## Evaluation Design\n\nEach test case should define:\n\n- `input`: realistic production-like input\n- `expected_contains`: required markers\u002Fcontent\n- `forbidden_contains`: disallowed phrases or unsafe content\n- `expected_regex`: required structural patterns\n\nThis enables deterministic grading across prompt variants.\n\n## Versioning Policy\n\n- Use semantic prompt identifiers per feature (`support_classifier`, `ad_copy_shortform`).\n- Record author + change note for every revision.\n- Never overwrite historical versions.\n- Diff before promoting a new prompt to production.\n\n## Rollout Strategy\n\n1. Create baseline prompt version.\n2. Propose candidate prompt.\n3. Run A\u002FB suite against same cases.\n4. Promote only if winner improves average and keeps violation count at zero.\n5. Track post-release feedback and feed new failure cases back into test suite.\n",{"name":5,"license":14,"metadata":59,"description":7},{"author":60,"updated":61,"version":62,"category":47},"Alireza Rezvani",1772755200000,"1.0.0","main","dc799ac68cfdc454fc55edaf3f054651f645f738","marketing-skill\u002Fprompt-engineer-toolkit",10,"https:\u002F\u002Fgithub.com\u002FArogyaReddy\u002Falirezarezvani-claude-skills","https:\u002F\u002Fgithub.com\u002FArogyaReddy","2026-03-18 00:40:19",{"source":8,"skillName":5,"commands":71,"prompt":81,"zipAvailable":82},[72,75,78],{"tool":73,"command":74},"npx","npx skills add ArogyaReddy\u002Falirezarezvani-claude-skills --skill prompt-engineer-toolkit",{"tool":76,"command":77},"bunx","bunx skills add ArogyaReddy\u002Falirezarezvani-claude-skills --skill prompt-engineer-toolkit",{"tool":79,"command":80},"pnpm","pnpm dlx skills add ArogyaReddy\u002Falirezarezvani-claude-skills --skill prompt-engineer-toolkit","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FArogyaReddy\u002Falirezarezvani-claude-skills\u002Fblob\u002Fmain\u002Fmarketing-skill\u002Fprompt-engineer-toolkit\u002FSKILL.md",true,[84,90,94,98,102,108],{"path":85,"resourceType":86,"resourceTypeDesc":87,"sizeBytes":88,"sourceHtmlUrl":89},"README.md","REFERENCE","参考文档",1244,"https:\u002F\u002Fgithub.com\u002FArogyaReddy\u002Falirezarezvani-claude-skills\u002Fblob\u002Fmain\u002Fmarketing-skill\u002Fprompt-engineer-toolkit\u002FREADME.md",{"path":91,"resourceType":86,"resourceTypeDesc":87,"sizeBytes":92,"sourceHtmlUrl":93},"references\u002Fevaluation-rubric.md",328,"https:\u002F\u002Fgithub.com\u002FArogyaReddy\u002Falirezarezvani-claude-skills\u002Fblob\u002Fmain\u002Fmarketing-skill\u002Fprompt-engineer-toolkit\u002Freferences\u002Fevaluation-rubric.md",{"path":95,"resourceType":86,"resourceTypeDesc":87,"sizeBytes":96,"sourceHtmlUrl":97},"references\u002Fprompt-templates.md",1532,"https:\u002F\u002Fgithub.com\u002FArogyaReddy\u002Falirezarezvani-claude-skills\u002Fblob\u002Fmain\u002Fmarketing-skill\u002Fprompt-engineer-toolkit\u002Freferences\u002Fprompt-templates.md",{"path":99,"resourceType":86,"resourceTypeDesc":87,"sizeBytes":100,"sourceHtmlUrl":101},"references\u002Ftechnique-guide.md",675,"https:\u002F\u002Fgithub.com\u002FArogyaReddy\u002Falirezarezvani-claude-skills\u002Fblob\u002Fmain\u002Fmarketing-skill\u002Fprompt-engineer-toolkit\u002Freferences\u002Ftechnique-guide.md",{"path":103,"resourceType":104,"resourceTypeDesc":105,"sizeBytes":106,"sourceHtmlUrl":107},"scripts\u002Fprompt_tester.py","SCRIPT","脚本",7793,"https:\u002F\u002Fgithub.com\u002FArogyaReddy\u002Falirezarezvani-claude-skills\u002Fblob\u002Fmain\u002Fmarketing-skill\u002Fprompt-engineer-toolkit\u002Fscripts\u002Fprompt_tester.py",{"path":109,"resourceType":104,"resourceTypeDesc":105,"sizeBytes":110,"sourceHtmlUrl":111},"scripts\u002Fprompt_versioner.py",8111,"https:\u002F\u002Fgithub.com\u002FArogyaReddy\u002Falirezarezvani-claude-skills\u002Fblob\u002Fmain\u002Fmarketing-skill\u002Fprompt-engineer-toolkit\u002Fscripts\u002Fprompt_versioner.py",[113,139,165,192,212,235],{"skillNo":114,"name":115,"nameZh":6,"description":116,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":117,"categories":118,"keywords":119,"publishedAt":54},"01M6EKFK0DB8WVYWCN59CM5SEQ","risk-management-specialist","Medical device risk management specialist implementing ISO 14971 throughout product lifecycle. Provides risk analysis, risk evaluation, risk control, and post-production information analysis. Use when user mentions risk management, ISO 14971, risk analysis, FMEA, fault tree analysis, hazard identification, risk control, risk matrix, benefit-risk analysis, residual risk, risk acceptability, or post-market risk.","https:\u002F\u002Fgithub.com\u002FArogyaReddy\u002Falirezarezvani-claude-skills\u002Fblob\u002Fmain\u002Fra-qm-team\u002Frisk-management-specialist\u002FSKILL.md",[],[120,121,122,123,124,125,126,127,128,129,130,45,131,132,133,134,135,136,137,138],"risk","risk management specialist","management","risk management","risk analysis","risk evaluation","product","Evaluation","Production","lifecycle","analysis","risk matrix","Market","control","iso","medical","iso 14971","identification","post",{"skillNo":140,"name":141,"nameZh":6,"description":142,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":143,"categories":144,"keywords":145,"publishedAt":54},"01M6EKFDZ7KES3X3HH8KBHR9JF","regulatory-affairs-head","Senior Regulatory Affairs Manager for HealthTech and MedTech companies. Prepares FDA 510(k), De Novo, and PMA submission packages; analyzes regulatory pathways for new medical devices; drafts responses to FDA deficiency letters and Notified Body queries; develops CE marking technical documentation under EU MDR 2017\u002F745; coordinates multi-market approval strategies across FDA, EU, Health Canada, PMDA, and NMPA; and maintains regulatory intelligence on evolving standards. Use when users need to plan or execute FDA submissions, navigate 510(k) or PMA approval processes, achieve CE marking, prepare pre-submission meeting materials, write regulatory strategy documents, respond to agency queries, or manage compliance documentation for medical device market access.","https:\u002F\u002Fgithub.com\u002FArogyaReddy\u002Falirezarezvani-claude-skills\u002Fblob\u002Fmain\u002Fra-qm-team\u002Fregulatory-affairs-head\u002FSKILL.md",[],[146,147,148,149,150,151,152,153,154,155,156,157,158,159,135,160,161,162,163,164],"regulatory affairs head","regulatory","head","documentation","documents","packages","plan","access","write","evidence","standards","intelligence","across","strategies","clinical","pathway","de","respond","medical devices",{"skillNo":166,"name":167,"nameZh":6,"description":168,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":169,"categories":170,"keywords":177,"publishedAt":54},"01M6EKF980P5MQBEQQAKB2JE6V","quality-manager-qms-iso13485","ISO 13485 Quality Management System implementation and maintenance for medical device organizations. Provides QMS design, documentation control, internal auditing, CAPA management, and certification support. Use when working with medical device quality systems, preparing for ISO 13485 audits, managing regulatory compliance documentation, setting up corrective actions, or building audit preparation programs. Useful for quality management, audit preparation, regulatory compliance, medical device documentation, and corrective action workflows.","https:\u002F\u002Fgithub.com\u002FArogyaReddy\u002Falirezarezvani-claude-skills\u002Fblob\u002Fmain\u002Fra-qm-team\u002Fquality-manager-qms-iso13485\u002FSKILL.md",[171,174],{"categoryNo":172,"categoryName":173,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":175,"categoryName":176,"taxonomyType":28,"taxonomyTypeDesc":29},"01JBSEEDCAT000000000000032","设计师",[178,179,180,181,182,52,149,183,184,185,186,45,187,188,189,133,134,135,190,191],"quality manager qms iso13485","quality","design","review","support","audit","actions","compliance","system","up","maintenance","document","records","ISO 13485",{"skillNo":193,"name":194,"nameZh":6,"description":195,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":196,"categories":197,"keywords":201,"publishedAt":54},"01M6EKF4ZN8ZJJP3K6FM1WEM85","quality-manager-qmr","Senior Quality Manager Responsible Person (QMR) for HealthTech and MedTech companies. Provides quality system governance, management review leadership, regulatory compliance oversight, and quality performance monitoring per ISO 13485 Clause 5.5.2.","https:\u002F\u002Fgithub.com\u002FArogyaReddy\u002Falirezarezvani-claude-skills\u002Fblob\u002Fmain\u002Fra-qm-team\u002Fquality-manager-qmr\u002FSKILL.md",[198],{"categoryNo":199,"categoryName":200,"taxonomyType":28,"taxonomyTypeDesc":29},"01JBSEEDCAT000000000000037","律师与法务",[202,179,181,203,204,205,206,185,207,186,122,208,147,209,210,211,134,191],"quality manager qmr","policy","performance","governance","monitoring","regulatory compliance","oversight","cost","culture","leadership",{"skillNo":213,"name":214,"nameZh":6,"description":215,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":216,"categories":217,"keywords":222,"publishedAt":54},"01M6EKF15YKH2AQB45MH4Y6S08","quality-documentation-manager","Document control system management for medical device QMS. Covers document numbering, version control, change management, and 21 CFR Part 11 compliance. Use for document control procedures, change control workflow, document numbering, version management, electronic signature compliance, or regulatory documentation review.","https:\u002F\u002Fgithub.com\u002FArogyaReddy\u002Falirezarezvani-claude-skills\u002Fblob\u002Fmain\u002Fra-qm-team\u002Fquality-documentation-manager\u002FSKILL.md",[218,221],{"categoryNo":219,"categoryName":220,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000002","文档处理",{"categoryNo":199,"categoryName":200,"taxonomyType":28,"taxonomyTypeDesc":29},[149,223,179,181,53,224,183,129,225,186,226,227,228,229,230,135,231,232,233,234],"quality documentation manager","change","document lifecycle","change management","list","master","system management","documentation review","cfr","21 cfr part 11","change control","retention",{"skillNo":236,"name":237,"nameZh":6,"description":238,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":239,"categories":240,"keywords":241,"publishedAt":54},"01M6EKEXQMCQC11A5WR9GEPC4S","qms-audit-expert","ISO 13485 internal audit expertise for medical device QMS. Covers audit planning, execution, nonconformity classification, and CAPA verification. Use for internal audit planning, audit execution, finding classification, external audit preparation, or audit program management.","https:\u002F\u002Fgithub.com\u002FArogyaReddy\u002Falirezarezvani-claude-skills\u002Fblob\u002Fmain\u002Fra-qm-team\u002Fqms-audit-expert\u002FSKILL.md",[],[183,242,243,244,245,246,247,248,122,45,249,250,134,135,191],"qms audit expert","expert","planning","verification","execution","classification","schedule","checklist","device"]